Large language models have shown considerable abilities across many tasks, but their capacity to detect sensitive user information from text raises significant privacy concerns. While recent approaches have explored sanitizing text to hide private features, a deeper challenge remains: distinguishing true privacy preservation from deceptive transformations. In this paper, we investigate whether LLM-based sanitization reduces private feature leakage without misleading an adversary into confidently predicting incorrect labels. Using LLM as both sanitizer and adversary, we measure leakage using two entropy-based metrics: Empirical Average Objective Leakage (E-AOL) and Empirical Average Confidence Boost (E-ACB). These allow us to quantify not only how accurate adversarial predictions are, but also how confident they remain post-sanitization. We posit that deception, while reducing adversarial accuracy, will also increase confidence in incorrect inferences, and hence reduced accuracy alone should not be interpreted as true privacy. We show that while current LLMs can hide private features, their transformations sometimes cause deception. Finally, we evaluate the semantic utility of sanitized outputs using sentence embeddings, LLM-based similarity judgments, and standard metrics like BLEU and ROUGE. Our findings emphasize the importance of explicitly distinguishing between privacy and deception in LLM-based sanitization and provide a framework for evaluating this distinction under realistic adversarial conditions.

Abstract In human society, the conflict between self-interest and collective well-being often obstructs efforts to achieve shared welfare. Related concepts like the Tragedy of the Commons and Social Dilemmas frequently manifest in our daily lives. As artificial agents increasingly serve as autonomous proxies for humans, we propose a novel multi-agent reinforcement learning (MARL) method to address this issue - learning policies to maximise collective returns even when individual agents’ interests conflict with the collective one. Unlike traditional cooperative MARL solutions that involve sharing rewards, values, and policies or designing intrinsic rewards to encourage agents to learn collectively optimal policies, we propose a novel MARL approach where agents exchange action suggestions. Our method reveals less private information compared to sharing rewards, values, or policies, while enabling effective cooperation without the need to design intrinsic rewards. Our algorithm is supported by our theoretical analysis that establishes a bound on the discrepancy between collective and individual objectives, demonstrating how sharing suggestions can align agents’ behaviours with the collective objective. Experimental results demonstrate that our algorithm performs competitively with baselines that rely on value or policy sharing or intrinsic rewards.

This work tackles the asymptotics of the maximal throughput of covert communications over AWGN channels when the covert metric is Kullback-Leibler divergence (KL divergence). It is shown that the first and second order asymptotics of the maximal throughput are$\sqrt{n\delta\log e}$and (2)${ }^{\frac{1}{2}}(n \delta)^{\frac{1}{4}}(\log e)^{\frac{3}{4}} \cdot Q^{-1}(\epsilon)$, respectively by$n$channel uses, where$\delta$and$\epsilon$are constraints imposed on covertness and channel decoding error probabilities, respectively. The technique we use in the achievability is quasi-$\varepsilon$-neighborhood notion from information geometry. For finite blocklength$n$, the generating distributions are chosen to be a family of truncated Gaussian distributions with decreasing variances. The law of decreasing is carefully designed so that it maximizes the throughput at the main channel in the asymptotic sense under the condition that the output distributions satisfy the covert constraint. For the converse, the optimality of Gaussian distribution for minimizing KL divergence under second order moment constraint is extended from dimension 1 to dimension$n$, which further leads to the direct converse bound in terms of covert metric.

We propose a novel problem formulation to address the privacy-utility tradeoff, specifically when dealing with two distinct user groups characterized by unique sets of private and utility attributes. Unlike previous studies that primarily focus on scenarios where all users share identical private and utility attributes and often rely on auxiliary datasets or manual annotations, we introduce a collaborative data-sharing mechanism between two user groups through a trusted third party. This third party uses adversarial privacy techniques with our proposed data-sharing mechanism to internally sanitize data for both groups and eliminates the need for manual annotation or auxiliary datasets. Our methodology ensures that private attributes cannot be accurately inferred while enabling highly accurate predictions of utility features. Importantly, even if analysts or adversaries possess auxiliary datasets containing raw data, they are unable to accurately deduce private features. Additionally, our data-sharing mechanism is compatible with various existing adversarially trained privacy techniques. We empirically demonstrate the effectiveness of our approach using synthetic and real-world datasets, showcasing its ability to balance the conflicting goals of privacy and utility.

We investigate the application of large language models (LLMs), specifically GPT-4, to scenarios involving the tradeoff between privacy and utility in tabular data. Our approach entails prompting GPT-4 by transforming tabular data points into textual format, followed by the inclusion of precise sanitization instructions in a zero-shot manner. The primary objective is to sanitize the tabular data in such a way that it hinders existing machine learning models from accurately inferring private features while allowing models to accurately infer utility-related attributes. We explore various sanitization instructions. Notably, we discover that this relatively simple approach yields performance comparable to more complex adversarial optimization methods used for managing privacy-utility tradeoffs. Furthermore, while the prompts successfully obscure private features from the detection capabilities of existing machine learning models, we observe that this obscuration alone does not necessarily meet a range of fairness metrics. Nevertheless, our research indicates the potential effectiveness of LLMs in adhering to these fairness metrics, with some of our experimental results aligning with those achieved by well-established adversarial optimization techniques.

The inevitable leakage of privacy as a result of unrestrained disclosure of personal information has motivated extensive research on robust privacy-preserving mechanisms. However, existing research is mostly limited to solving the problem in a static setting with disregard for the privacy leakage over time. Unfortunately, this treatment of privacy is insufficient in practical settings where users continuously disclose their personal information over time resulting in an accumulated leakage of the users’ sensitive information. In this paper, we consider privacy leakage over a finite time horizon and investigate optimal strategies to maximize the utility of the disclosed data while limiting the finite-horizon privacy leakage. We consider a simple privacy mechanism that involves compressing the user’s data before each disclosure to meet the desired constraint on future privacy. We further motivate several algorithms to optimize the dynamic privacy-utility tradeoff and evaluate their performance via extensive synthetic performance tests.

Knowledge graphs (KGs) can help enhance recommendations, especially for the data-sparsity scenarios with limited user-item interaction data. Due to the strong power of representation learning of graph neural networks (GNNs), recent works of KG-based recommendation deploy GNN models to learn from both knowledge graph and user-item bipartite interaction graph. However, these works have not well considered the hierarchical structure of knowledge graph, leading to sub-optimal results. Despite the benefit of hierarchical structure, leveraging it is challenging since the structure is always partly-observed. In this work, we first propose to reveal unknown hierarchical structures with a supervised signal detection method and then exploit the hierarchical structure with disentangling representation learning. We conduct experiments on two large-scale datasets, of which the results well verify the superiority and rationality of the proposed method. Further experiments of ablation study with respect to key model designs have demonstrated the effectiveness and rationality of our proposed model. The code is available at https://github.com/tsinghua-fib-lab/HIKE .

Location prediction acts as a fundamental service in today's location-based information platform, which helps users access locations satisfying their demands, improving both user experience and platform profit. Since users with unambiguous demands prefer specific locations while users with compound demands consider first regions and then specific locations, it is necessary to model multi-grained user preferences at different geographical scales. However, most of the existing works concentrate on user preferences at the location-scale only, which can not understand users traveling behaviors thoroughly. In this paper, we propose to model both the fine-grained user preferences at the location scale and the coarsegrained user preferences at the region scale. Specifically, the proposed model harnesses the efficient information extraction power of graph neural networks. Moreover, the proposed geographical calibration method also helps to capture multi-grained user preferences accurately. Experiments on datasets of two very large cities demonstrate the significant performance improvement using our approach over state-of-the-art models. We also conduct experiments to further demonstrate the effectiveness of each component in the proposed model. Source codes of this paper are available at https://github.com/tsinghua-fib-lab/SIGSPATIAL-MMGUP/.

Point-of-Interest (POI) recommendation has drawn a lot of attention in both academia and industry. It utilizes user check-in data, aiming at recommending unvisited POIs to users. To address the data-sparsity problem, geographical information of POIs is often incorporated into recommender systems. However, most of the existing approaches model geographical impact in an implicit way, in which geographical information is encoded as auxiliary vectors for learning unified representations of users and POIs. Following this paradigm, the embedding of POIs can not reflect geographical similarity directly; thus, an explicit modeling approach is needed as geography is of great importance in POI recommendation. To address challenges in disentangling geographical effect, we proposed a disentangled representation learning method named DIG (short for Disentangled embedding of user Interest and POIs' Geographical information). Aiming at decoupling the geographical factor and the user interest factor thoroughly, we first proposed a geo-constrained negative sampling strategy, which helps to find reliable negative samples for the two factors. Second, a geo-enhanced soft-weighted loss function was proposed to quantify the trade-off between the two factors in loss computation. Extensive experiments have been conducted on two real-world datasets, and results have demonstrated the significant improvement of DIG at 3.92% - 20.32% 3.92% - 20.32% on recall, and 2.53% - 11.48% 2.53% - 11.48% on hit ratio, compared with other state-of-the-art approaches.

We solve an important and challenging cooperative navigation control problem, Multiagent Navigation to Unassigned Multiple targets (MNUM) in unknown environments with minimal time and without collision. Conventional methods are based on multiagent path planning that requires building an environment map and expensive real-time path planning computations. In this article, we formulate MNUM as a stochastic game and devise a novel multiagent deep reinforcement learning (MADRL) algorithm to learn an end-to-end solution, which directly maps raw sensor data to control signals. Once learned, the policy can be deployed onto each agent, and thereby, the expensive online planning computations can be offloaded. However, to solve MNUM, traditional MADRL suffers from large policy solution space and nonstationary environment when agents make decisions independently and concurrently. Accordingly, we propose a hierarchical and stable MADRL algorithm. The hierarchical learning part introduces a two-layer policy model to reduce the solution space and uses an interlaced learning paradigm to learn two coupled policies. In the stable learning part, we propose to learn an extended action-value function that implicitly incorporates estimations of other agents' actions, based on which the environment's nonstationarity caused by other agents' changing policies can be alleviated. Extensive experiments demonstrate that our method can converge in a fast way and generate more efficient cooperative navigation policies than comparable methods.

We propose an adversarial learning framework that deals with the privacy-utility tradeoff problem under two types of conditions: data-type ignorant, and data-type aware. Under data-type aware conditions, the privacy mechanism provides a one-hot encoding of categorical features, representing exactly one class, while under data-type ignorant conditions the categorical variables are represented by a collection of scores, one for each class. We use a neural network architecture consisting of a generator and a discriminator, where the generator consists of an encoder-decoder pair, and the discriminator consists of an adversary and a utility provider. Unlike previous research considering this kind of architecture, which leverages autoencoders (AEs) without introducing any randomness, or variational autoencoders (VAEs) based on learning latent representations which are then forced into a Gaussian assumption, our proposed technique introduces randomness and removes the Gaussian assumption restriction on the latent variables, only focusing on the end-to-end stochastic mapping of the input to privatized data. We test our framework on different datasets: MNIST, FashionMNIST, UCI Adult, and US Census Demographic Data, providing a wide range of possible private and utility attributes. We use multiple adversaries simultaneously to test our privacy mechanism - some trained from the ground truth data and some trained from the perturbed data generated by our privacy mechanism. Through comparative analysis, our results demonstrate better privacy and utility guarantees than the existing works under similar, data-type ignorant conditions, even when the latter are considered under their original restrictive single-adversary model.

It is well known that finite blocklength analysis plays an important role in evaluating performances of communication systems in practical settings. This paper considers the achievability and converse bounds on the maximal channel coding rate (throughput) at a given blocklength and error probability in covert communication over AWGN channels. The covert constraint is given in terms of an upper bound on total variation distance (TVD) between the distributions of eavesdropped signals at an adversary with and without presence of active and legitimate communication, respectively. For the achievability, Gaussian random coding scheme is adopted for convenience in the analysis of TVD. The classical results of finite blocklength regime are not applicable in this case. By exploiting and extending canonical approaches, we first present new and more general achievability bounds for random coding schemes under maximal or average probability of error requirements. The general bounds are then applied to covert communication in AWGN channels where codewords are generated from Gaussian distribution while meeting the maximal power constraint. We further show an interesting connection between attaining tight achievability and converse bounds and solving two total variation distance based minimax and maxmin problems. The TVD constraint is analyzed under the given random coding scheme, which induces bounds on the transmission power through divergence inequalities. Further comparison is made between the new achievability bounds and existing ones derived under deterministic codebooks. Our thorough analysis thus leads us to a comprehensive characterization of the attainable throughput in covert communication over AWGN channels.

Deep reinforcement learning (DRL) is able to learn control policies for many complicated tasks, but it's power has not been unleashed to handle multi-agent circumstances. Independent learning, where each agent treats others as part of the environment and learns its own policy without considering others' policies is a simple way to apply DRL to multi-agent tasks. However, since agents' policies change as learning proceeds, from the perspective of each agent, the environment is non-stationary, which makes conventional DRL methods inefficient. To cope with this challenge, we propose a novel approach where each agent uses an implicit estimate of others' actions to guide its own policy learning. We demonstrate that given the implicit estimate of others' actions, each agent can learn its policy in a relatively stationary environment. Extensive experiments show that our method significantly alleviates the non-stationarity and outperforms the state-of-the-art in terms of both convergence speed and policy performance.

We explore the algebraic structure of the solution space of convex optimization problem Constrained Minimum Trace Factor Analysis (CMTFA), when the population covariance matrix Σxhas an additional latent graphical constraint, namely, a latent star topology. In particular, we have shown that CMTFA can have either a rank 1 or a rank n - 1 solution and nothing in between. The special case of a rank 1 solution, corresponds to the case where just one latent variable captures all the dependencies among the observables, giving rise to a star topology. We found explicit conditions for both rank 1 and rank n - 1 solutions for CMTFA solution of Σx. As a basic attempt towards building a more general Gaussian tree, we have found a necessary and a sufficient condition for multiple clusters each having rank 1 CMTFA solution to satisfy a minimum probability, to combine together to build a Gaussian tree.

Despite the benefits of electric and autonomous vehicles, current in-vehicle networks lack a robust and feasible security framework that considers the authentication, confidentiality, and integrity for the communication of Electronic Control Units (ECUs). Although a centralized key management mechanism offers an efficient solution, the security fully relies on this centralized unit, which leads to a single point of failure problem. In this paper, we present a semi-centralized key management framework to secure in-vehicle networks. It provides a decentralized and dynamic key distribution during the vehicle's operation and considers different aspects such as ECU's broadcast communication, ECU manufacturing process and ECU authentication without the use of certificates from external third parties. Finally, the implementation and the simulation of our framework validate the feasibility and practical use of our approach.

The Constrained Minimum Determinant Factor Analysis (CMDFA) setting was motivated by Wyner's common information problem where we seek a latent representation of a given Gaussian vector distribution with the minimum mutual information under certain generative constraints. In this paper, we explore the algebraic structures of the solution space of the CMDFA, when the underlying covariance matrix Σxhas an additional latent graphical constraint, namely, a latent star topology. In particular, sufficient and necessary conditions in terms of the relationships between edge weights of the star graph have been found. Under such conditions and constraints, we have shown that the CMDFA problem has either a rank one solution or a rank n-1 solution where n is the dimension of the observable vector. Numerical results are provided to demonstrate the difference between the optimal mutual information and that derived under a naive star constraint.

Traditional approaches to secret key establishment based on common randomness have been based on certain restrictive assumptions, such as considering the available common randomness to consist of independent and identically distributed (i.i.d) repetitions of correlated random variables. Unfortunately, the i.i.d assumption does not generally reflect the conditions of real-life scenarios. For this reason, the current paper investigates the key-establishment potential of a more pragmatic model, in which all parties have access to imperfect information about a common source modeled as a Markov chain. Each party's information thus comes in the form of a hidden Markov model and, since the different parties share the same underlying Markov chain, we call the overall model a sibling hidden Markov model (SHMM). This paper studies upper and lower bounds on the secret key capacity for various types of SHMM. The difficulty of the problem emerges from its prohibitive computational cost. To address this obstacle, we represent the joint probability of the observations as the L1norm of a Markov random matrix, and use its convergence to a Lyapunov exponent.

Radio Frequency IDentification (RFID) systems are prevalent in all sorts of daily life endeavors. Most previous tag estimation schemes worked with relatively smaller frame size and large number of rounds. Here we propose a new estimator named \textquotedblleft Gaussian Estimator of RFID Tags,\textquotedblright (GERT), that works with large enough frame size to be accurately approximated to Gaussian distribution within a frame. The selection of the frame size is done according to Triangular Array Central Limit Theorem which also enables us to quantify the approximation error. Larger frame size helps the statistical average to converge faster to the ensemble mean of the estimator and the quantification of the approximation error helps to determine the number of rounds to keep up with the accuracy requirements. The overall performance of GERT is better than the previously proposed schemes considering the number of slots required for estimation to achieve a given level of estimation accuracy.

In this paper, we propose a novel framework to study the problem of sequential detection of active substructures under the constraint of protection of edge-wise activity patterns. We begin by offering a definition of privacy within this framework as a means to a better interpretation of the constraints concerning our paper. We then formulate the novel problem of detecting active subgraphs from a given set of link-wise disjoint substructures. We show how such active graphs could be identified by querying with a series of feasible binary queries satisfying the constraint of protecting link-wise states over the detection period, a feat whose representation is further interpreted using vertex covering of subgraphs. Furthermore, we introduce the relationship between partial and complete vertex covering and the resulting breach of privacy imposed by the latter in our problem. A random coding approach is proposed to establish a sequential and non-adaptive binary search process whose average stopping time is analyzed based on both upper and lower bounds. Then the complexity of the method is calculated and shown to be efficient. Finally, the simulation results are provided to demonstrate the efficiency of proposed bounds.

Secret key establishment from common randomness has been traditionally investigated under cartain limiting assumptions, of which the most ubiquitous appears to be that the information available to all parties comes in the form of independent and identically distributed (i.i.d.) samples of some correlated random variables. Unfortunately, models employing the i.i.d assumption are often not accurate representations of real scenarios. A more capable model would represent the available information as correlated hidden Markov models (HMMs), based on the same underlying Markov chain. Such a model accurately reflects the scenario where all parties have access to imperfect observations of the same source random process, exhibiting a certain time dependency. In this paper, we derive a computationally-efficient asymptotic converse bound for the secret key capacity of the correlated-HMM scenario. The main obstacle, not only for our model, but also for other non-i.i.d cases, is the computational complexity. We address this by converting the initial bound to a product of Markov random matrices, and using recent results regarding its convergence to a Lyapunov exponent. The methods developed in the paper are easily extensible to derive a secret-key capacity lower bound.

In this paper, our objective is to find out the determining factors of Chernoff information in distinguishing a set of Gaussian trees. In this set, each tree can be attained via a subtree removal and grafting operation from another tree. This is equivalent to asking for the Chernoff information between the most-likely confused, i.e. “bottleneck”, Gaussian trees, as shown to be the case in ML estimated Gaussian tree graphs lately. We prove that the Chernoff information between two Gaussian trees related through a subtree removal and grafting operation is the same as that between two three-node Gaussian trees, whose topologies and edge weights are subject to the underlying graph operation. In addition, such Chernoff information is shown to be determined only by the maximum generalized eigenvalue of the two Gaussian covariance matrices. The Chernoff information of scalar Gaussian variables as a result of linear transformation (LT) of the original Gaussian vectors is also uniquely determined by the same maximum generalized eigenvalue. What is even more interesting is that after incorporating the cost of measurements into a normalized Chernoff information, Gaussian variables from LT have larger normalized Chernoff information than the one based on the original Gaussian vectors, as shown in our proved bounds.

In this paper, we study both topological and algebraic properties of unrooted Gaussian trees in order to characterize their security performance. Such performance is measured by the corresponding potential in extracting common randomness from a given tree, which is further determined by max-min and min-max conditional mutual information (CMI) values, subject to the order of selecting variables from the tree by legitimate nodes Alice and Bob, and an eavesdropper Eve, respectively. A new operation is proposed to transform a Gaussian tree into another, and also to order different Gaussian trees. Through such operation we construct several equivalent classes of Gaussian trees. Each class includes multiple Gaussian trees that can be partially ordered based on the associated max-min or min-max CMI metric, and thus, we can find the most secure and the least secure trees in each partially ordered set (poset). The union of all posets generates all possible non-isomorphic trees of the given number of variables. Then, we assign a particular polynomial to each Gaussian tree, and show that such polynomial can determine the relative security performance of the Gaussian tree with respect to other trees within the same class. In the end, based on a generalized integer partition method, we propose a novel approach to efficiently enumerate the most secure structures of all posets.

Establishing secret common randomness between two or multiple devices in a network resides at the root of communication security. In its most frequent form of key establishment, the problem is traditionally decomposed into a randomness generation stage (randomness purity is subject to employing often costly true random number generators) and an information-exchange agreement stage, which relies either on public-key infrastructure or on symmetric encryption (key wrapping). In this paper, we propose a secret-common-randomness establishment algorithm for ad hoc networks, which works by harvesting randomness directly from the network routing metadata, thus achieving both pure randomness generation and (implicitly) secret-key agreement. Our algorithm relies on the route discovery phase of an ad hoc network employing the dynamic source routing protocol, is lightweight, and requires relatively little communication overhead. The algorithm is evaluated for various network parameters in an OPNET ad hoc network simulator. Our results show that, in just 10 min, thousands of secret random bits can be generated network-wide, between different pairs in a network of 50 users.

In this paper, our objective is to find out how topological and algebraic properties of unrooted Gaussian tree models determine their security robustness, which is measured by our proposed max-min information (MaMI) metric. Such metric quantifies the amount of common randomness extractable through public discussion between two legitimate nodes under an eavesdropper attack. We show some general topological properties that the desired max-min solutions shall satisfy. Under such properties, we develop conditions under which comparable trees are put together to form partially ordered sets (posets). Each poset contains the most favorable structure as the poset leader, and the least favorable structure. Then, we compute the Tutte-like polynomial for each tree in a poset in order to assign a polynomial to any tree in a poset. Moreover, we propose a novel method, based on restricted integer partitions, to effectively enumerate all poset leaders. The results not only help us understand the security strength of different Gaussian trees, which is critical when we evaluate the information leakage issues for various jointly Gaussian distributed measurements in networks, but also provide us both an algebraic and a topological perspective in grasping some fundamental properties of such models.

Due to the huge popularity of online social networks, many researchers focus on adding links, e.g., link prediction to help friend recommendation. So far, no research has been performed on link cuts. However, the spread of malware and misinformation can cause havoc and hence it is interesting to see how to cut links such that malware and misinformation will not run rampant. In fact, many online social networks can be modeled as undirected graphs. In this paper, we investigate different strategies to cut links among different users in undirected graphs so that the speed of virus and misinformation spread can be slowed down the most or even cut off. Two measures are chosen to evaluate the performance of these strategies: Average Inverse of Shortest Path Length (AIPL) and Rumor Saturation Rate (RSR). AIPL measures the communication efficiency of the whole graph while RSR checks the percentage of users receiving information within a certain time interval.

In this paper, we propose a novel model to study the efficiency of detecting latent connection relationships, represented by a given set of graphs, among N users. A subset of active nodes transmit following a common codebook over a multiple access Boolean channel. To maximize the error exponent of the structure detection, we formulate an optimization problem whose objective is to max-minimize the pairwise Chernoff information, and the constraint is a probability simplex due to the users' multiple dependency relationships, which are further shown to have close relationship to the internal connectivity of graphs. Case studies are provided to show certain inherent properties of the optimal solution. In addition, we present a particular case with two equally weighted complementary Paley graphs of prime square order, whose optimal solution for the codebook is proved and the resulting exponent is shown to be O(1/N). The case study demonstrates how the fundamental graph discrepancy property affects the solution to the problem.

We present a novel model for multiple access systems that jointly models the access-control and communication layers. This model helps us jointly quantify both the collision loss at the control layer and congestion loss at the communication layer. A joint quantification of both losses is necessary because the performance of one layer directly impacts that of the other one, and thus, both these layers are inseparable. However, the conventional theoretical models quantify the collision and congestion losses separately, thereby unable to capture the coupling issues in networks with finite sources' constraint. Under our proposed framework, we further optimize the number of control and communication channels in order to minimize the joint total loss rate given a constraint on the total number of available channels. The corresponding optimization results are further visualized using our proposed channel allocation map under all possible traffic parameters needed in the considered system model.

Extensive works have been undertaken to develop efficient statistical inference algorithms based on graphical models. However, there still lacks sufficient understanding about how topological properties affect certain information related metrics for certain graphs. In this paper, we are particularly interested in finding out how topological properties of rooted polytrees for Gaussian random variables determine its security robustness, which is measured by our proposed max-min information (MaMI) metric. MaMI is defined as the maximin value of the conditional mutual information between any two random variables (nodes) in a given DAG, conditioned on the value of a third random variable, which is at full disposal of an eavesdropper, under a constraint of a given fixed joint entropy. We show some general topological properties which the desired max-min solutions satisfy. Under such properties, we prove the superior max-min feature of the linear topology for a simple but non-trivial case. The results not only help us understand the security strength of different rooted polytree type DAGs, which is critical when we evaluate the information leakage issues for various jointly Gaussian distributed measurements in networks, but also provide us another algebraic and analysis perspective in grasping some fundamental properties of such DAGs.

It is well known that avalanche effect errors in received noisy ciphertexts will cause severe error propagation in block-ciphered encryption systems, thus resulting in a large reduction in the achievable throughput. However, little is known about the statistical properties of the underlying error sequences in decrypted plaintexts in block-ciphered systems when channel errors are present. A rigorous study of the statistical properties of the errors in block-ciphered crypto-systems operating in cipher block chaining (CBC) mode is provided. The equivalent channel transition probability is obtained and then used to derive error statistics including both error weight probabilities and gap distributions. The validity of the theoretical analyses is confirmed by the excellent match with results obtained by simulated data encryption standard (DES)-based and advanced encryption standard (AES)-based crypto-systems operating in CBC mode. The error statistics will be valuable in the design and performance evaluation of communication protocols, as well as in error-control schemes for block-ciphered crypto-systems in the presence of erroneous ciphertexts, where errors are intentionally left to enhance security against passive eavesdroppers.

In this paper, we propose a novel reservation system to study partition information and its transmission over a noise-free Boolean multiaccess channel. The objective of transmission is not to restore the message, but to partition active users into distinct groups so that they can, subsequently, transmit their messages without collision. We first calculate (by mutual information) the amount of information needed for the partitioning without channel effects, and then propose two different coding schemes to obtain achievable transmission rates over the channel. The first one is the brute force method, where the codebook design is based on centralized source coding; the second method uses random coding, where the codebook is generated randomly and optimal Bayesian decoding is employed to reconstruct the partition. Both methods shed light on the internal structure of the partition problem. A novel formulation is proposed for the random coding scheme, in which a sequence of channel operations and interactions induces a hypergraph. The formulation intuitively describes the transmitted information in terms of a strong coloring of this hypergraph. An extended Fibonacci structure is constructed for the simple, but nontrivial, case with two active users. A comparison between these methods and group testing is conducted to demonstrate the potential of our approaches.

In this paper, we consider the problem of partitioning active users in a manner that facilitates multi-access without collision. The setting is of a noisy, synchronous, Boolean, and multi-access channel, where K active users (out of a total of N users) seek channel access. A solution to the partition problem places each of the N users in one of K groups (or blocks), such that no two active nodes are in the same block. We consider a simple, but non-trivial and illustrative, case of K = 2 active users and study the number of steps T used to solve the partition problem. By random coding and a suboptimal decoding scheme, we show that for any T ≥ (C1+ ξ1) log N, where C1and ξ1are positive constants (independent of N), and where ξ1can be arbitrary small, the partition problem can be solved with error probability Pe(N)→ 0, for large N. Under the same scheme, we also bound T from the other direction, establishing that, for any T ≤ (C2- ξ2) log N, the error probability Pe(N)→ 1 for large N; again, C2and ξ2are constants, and ξ2can be arbitrarily small. These bounds on the number of steps are lower than the tight achievable lower bound in terms of T ≥ (Cg+ ξ) log N for group testing (in which all active users are identified, rather than just partitioned). Thus, partitioning may prove to be a more efficient approach for multi-access than group testing.

In this paper, we formulate a novel problem to quantify the amount of information transferred to partition active users who transmit following a common codebook over noisy Boolean multi-access channels. The objective of transmission is to ultimately let each active user aware of its own group only, not others. To solve the problem, we propose a novel framework by considering the decoding as a process of removing hyperedges of a complete hypergraph. For a particular, but non-trivial, case with two active users, an achievable bound for the defined partition information rate is found by using strong typical set decoding, as well as a large deviation technique for an induced Markov chain.

This paper focuses on the problem of sharing secret keys using Automatic Repeat reQuest (ARQ) protocol. We consider cases where forward and feedback channels are erasure channels for a legitimate receiver (Bob) and an eavesdropper (Eve). In prior works, the wiretap channel is modeled as statistically independent packet erasure channels for Bob and Eve. In this paper, we go beyond the state-of-the-art by addressing correlated erasure events across the wiretap channel. The created randomness is shared between two legitimate parties through ARQ transmissions that is mapped into a destination set using a first-order digital filter with feedback. Then, we characterize Eve's information loss about this shared destination set, due to inevitable transmission errors. This set is then transformed into a highly secure key using privacy amplification in order to intensify and exploit Eve's lack of knowledge. We adopt two criteria for analysis and design of the system: secrecy outage probability as a measure of the secrecy quality, and secret key rate as a metric for efficiency. The resulting secrecy improvement is presented as a function of the correlation coefficients and the erasure probabilities for both channels. It is shown that secrecy improvement is achievable even when Eve has a better channel than legitimate receivers, and her channel conditions are unknown to legitimate users.

It has long been held that errors in received noisy ciphertexts should be eliminated using as many as possible powerful error correcting codes in order to reduce the avalanche effect on legitimate users' performance in block ciphered systems. However, the negative effect of erroneous ciphertexts on cryptanalysis by an eavesdropper has not been well understood, nor the possible measurable trade-off between security enhancement and performance degradation under noisy ciphertexts. To address these questions, we have launched a case study in this paper using Data Encryption Standard (DES)-based block ciphers operating in cipher feedback (CFB) mode to show quantitatively the pros and cons of exploiting voluntarily or nonvoluntarily introduced binary errors in ciphertexts of block ciphered systems using our proposed comparison metrics. A serially concatenated scheme with both outer and inner encoder-encipher pairs is proposed which allows us to quantitatively reveal the sacrifice made by legitimate users in its postdecryption capacity, as well as the security improvement factor (SIF) which reflects the additionally required plaintext-ciphertext pairs for eavesdropper's known plaintext attack, in the presence of noise in ciphertexts. Simulation results demonstrate the accuracy of derived approximations of the postdecryption performance for the legitimate receiver.

In this paper, we study the effect of channel errors on the performance of linear cryptanalysis against block ciphered system. We study DES block cipher working in cipher feedback mode (CFB) as a special case. In our model, eavesdropper launches linear attack by querying an oracle which provides her with corrupted ciphertexts over a binary symmetric channel (BSC). A new verification strategy in linear attack has been designed and numerically optimized to allow Eve to mount a successful attack in noisy environments. However, we show that even by utilizing this optimized strategy, there is still possibility of misdetection in Eve's cryptanalysis, which directly depends on the channel degradation level. Numerical results show that the proposed attack strategy lets Eve maintain a high performance even for relatively high noise levels. On the other hand, they suggest that due to Eve's possible failures in her attack, tunable cross over probability of the channel can bring about the lowest performance for Eve as well as a higher security.

Consider a parallel channel with M independent flat-fading subchannels. There exists a smart jammer which has possession of a copy of perfect channel state information (CSI) measured and sent back by a receiver to its transmitter. Under this model, a class of two-person zero-sum games is investigated where either achievable mutual information rate or Chernoff bound is taken as the underlying pay-off function with the strategy space of each player determined by respective power control and hopping functions. More specifically, we have tackled and answered the following three fundamental questions. The first one is about whether the transmitter and jammer should hop or fully use all degrees of freedom over the entire parallel channels given the full CSI available to both of them, i.e. to hop or not to hop. The second question is about the impact of sending back CSI on system performance considering that the smart jammer can exploit CSI to further enhance its interference effects, i.e. to feedback or not to feedback. The last question is about whether the amount of feedback information can be reduced given the mutual restrictions between transmitter and jammer, i.e. when to feedback and when not to.

This paper considers the problem of end-to-end security enhancement by resorting to deliberate noise injected in ciphertexts. The main goal is to generate a degraded wiretap channel in the application layer over which Wyner-type secrecy encoding is invoked to deliver additional secure information. More specifically, we study secrecy enhancement of the Data Encryption Standard (DES) block cipher working in cipher feedback model (CFB) when adjustable noise is introduced into the encrypted data in an application layer. A verification strategy in the exhaustive search step of the linear attack is designed to allow Eve to mount a successful attack in the noisy environment. Thus, a controllable wiretap channel is created over multiple frames by taking advantage of errors in Eve's cryptanalysis, whose secrecy capacity is found for the case of known channel states at receivers. As a result, additional secure information can be delivered by performing Wyner type secrecy encoding over superframes ahead of encryption. These secrecy bits could be taken as symmetric keys for upcoming frames. Numerical results indicate that a sufficiently large secrecy rate can be achieved by selective noise addition.

In this paper, we study the problem of half-duplex active eavesdropping in fast-fading channels. The active eavesdropper is a more powerful adversary than the classical eavesdropper. It can choose between two functional modes: eavesdropping the transmission between the legitimate parties (Ex mode), and jamming it (Jx mode)-the active eavesdropper cannot function in full duplex mode. We consider a conservative scenario, when the active eavesdropper can choose its strategy based on the legitimate transmitter-receiver pair's strategy, and thus, the transmitter and legitimate receiver have to plan for the worst. We show that conventional physical-layer secrecy approaches perform poorly (if at all), and we introduce a novel encoding scheme, based on very limited and unsecured feedback-the Block-Markov Wyner encoding scheme-which outperforms any schemes currently available.

In this paper, we propose a feedback scheme for transmitting secret messages between two legitimate parties, over an eavesdropped communication link. Relative to Wyner's traditional encoding scheme, our feedback-based encoding often yields larger rate-equivocation regions and achievable secrecy rates. More importantly, by exploiting the channel randomness inherent in the feedback channels, our scheme achieves a strictly positive secrecy rate even when the eavesdropper's channel is less noisy than the legitimate receiver's channel. All channels are modeled as binary and symmetric. We demonstrate the versatility of our feedback-based encoding method by using it in three different configurations: the stand-alone configuration, the mixed configuration (when it combines with Wyner's scheme), and the reversed configuration. Depending on the channel conditions, significant improvements over Wyner's secrecy capacity can be observed in all configurations.

In this paper, we provide a fundamental channel model to characterize the interference effect inherent in cognitive radio systems. Mutual information rates of our proposed probabilistic block interference channels for both primary and secondary users are derived without assuming that receivers have knowledge on channel interference states. Novel constrained optimization problems are then put forward with a constraint on the performance loss margin tolerated by the primary user. Furthermore, we investigate some special cases where conditions are provided to justify the optimality of adopting Neyman-Pearson rule. Also presented are some scenarios in which randomized decision without using sensing measurement is needed to balance the rateloss for PU and throughput gain for SD.

This paper studies the problem of active eavesdropping in fast fading channels. The active eavesdropper (Eve-A) is a more powerful adversary than the classical eavesdropper. It can choose between two functional modes: eavesdropping (Ex mode), and jamming (Jx mode)-Eve-A cannot function in full duplex mode. We consider the most conservative scenario, when the Eve-A can choose her strategy based on the legitimate transmitter-receiver pair's strategy-and thus the transmitter and legitimate receiver have to plan for the worst. We introduce a novel encoding scheme, based on very limited and unprotected feedback-the Block-Markov Wyner (BMW) encoding scheme-which outperforms any schemes currently available.

Orthogonal frequency division multiplexing (OFDM) systems have been used extensively in wireless communications in recent years; thus, there is significant interest in analyzing the properties of the transmitted signal in such systems. In particular, a large amount of work has focused on analyzing the variation of the complex envelope of the transmitted signal and on designing methods to minimize this variation. In this paper, it is established that the complex envelope of a bandlimited uncoded OFDM signal converges weakly to a Gaussian random process as the number of subcarriers goes to infinity. This shows that the properties of the OFDM signal will asymptotically approach those of a Gaussian random process over any finite time interval. The convergence proof is then extended to two important cases, namely, coded OFDM systems and systems with an unequal power allocation across subcarriers.

We study the problem of jamming in a fixed-rate wireless system over fast fading channels. Both transmitter and jammer are subject to long term (average) power constraints. Our jamming problem is formulated as a zero-sum game, with the probability of outage as pay-off function and power control functions as strategies. We consider both the case with full channel state information (CSI) at all parties (available from a training and feedback protocol), and the case when no CSI is fed back from the receiver. Nash equilibria of mixed strategies are found by solving the generalized form of an older problem dated back to Bell and Cover.

In this paper, we adopt outage probability (lambda-capacity) in fast fading channels as a pay-off function in a zero- sum game between a legitimate transceiver pair and an uncorrelated Gaussian jammer. The transmitter aims at minimizing the outage probability, while the jammer attempts to maximize the outage probability. We consider both peak (over each codeword) and average (over all codewords) power constraints. For peak power constraints, a transmission rate is either supported by the system, or if too large, causes the whole transmission to fail. By imposing average power constraints, large rates can be supported at the cost of positive probability of codeword error. Maxmin and minimax power control strategies are developed, which show that no Nash equilibrium of pure strategies exists under average power constraints.

In this paper, we first provide a joint source and channel coding (JSCC) approach in estimating Gaussian sources over Gaussian MAC channels, as well as its sufficient and necessary condition in restoring Gaussian sources with a prescribed distortion value. An interesting relationship between our proposed joint approach with a more straightforward separate source and channel coding (SSCC) scheme is further established. We then formulate constrained power minimization problems to minimize total transmission power consumption under a distortion constraint for arbitrary in-homogeneous networks under JSCC, SSCC and uncoded scheme (UC). They are transformed to relaxed convex geometric programming problems. Our numerical results exhibit that none of the three schemes is consistently most energy efficient. The proposed JSCC could be more energy efficient than either the uncoded scheme, or SCCC, but not both. In addition, we prove that the optimal decoding order to minimize the total transmission powers for both source and channel coding parts is solely subject to the ordering of MAC channel qualities, and has nothing to do with the ranking of measurement qualities across measuring nodes.

In the existing works on distributed detection in sensor networks, local sensor nodes either quantize the observation or directly scale the analog observation and then transmit the processed information independently over wireless channels to a fusion center. In this paper, we exploit the advantages of both of these two approaches by constructing an equivalent Sigma-Delta ADC across space over wireless sensor networks. Sensors are arranged in a mixing of parallel and serial topologies, enabling each sensor to transmit binary information to the fusion center, while in the meantime preserving the analog information through collaborative processing. Comparison with existing approaches demonstrates the superiority of our proposed scheme in both AWGN and fading channels in terms of the resulting detection error probability.

Relaying is often advocated for improving system performance by enhancing spatial diversity in wireless networks. In this paper, we address the issue of energy tradeoff made by relay nodes between transmitting their own data and forwarding other nodes' information in fading channels. We first propose a power control policy in a two-node relay network under which total energy consumption across both nodes is minimized while meeting both outage probability requirements. Based on this power control algorithm, we consider the problem of forming optimal partial coalitions of relays in an N node system subject to selfish constraints: A node participates in a relay pair (or chain) if and only if the energy cost of relaying is lower than the cost of direct transmission by the node to the destination itself. We develop a simple (1,2)-polynomial time bi-criteria approximation for this NP-hard problem. The energy cost provided by the approximation is at most that of the optimal relay pairing, while the constraints are violated by at most a factor of two. The running time of the approximation algorithm is polynomial, as it requires the solution of a relaxed linear programming instance of the original integer programming problem.

To address the coordination issue of sensors communicating with a fusion center, we propose a spreading sequence based non-coherent detection scheme for sensor networks to reduce the coordination between sensors to the largest extent. In this scheme, sensors employ independent spreading sequences to transmit their measurements. Non-coherent detection is conducted at the fusion center where only statistics regarding channel gains and sensor measurement uncertainties are needed. To evaluate the detector's performance, we first derive the large deviation exponents of detection error probabilities and then compare them with the approaches assuming orthogonal channel allocation (e.g. TDMA/FDMA). Numerical and simulation results demonstrate the dependence of large deviation exponent on the asymptotic number of sensors per chip (defined as c), as well as the better performance of our proposed scheme than the one using non-coherent detection with orthogonal link, for some c.

We formulate a Gaussian uncorrelated jamming problem in block fading channels under long term power constraints. Source aims at minimizing the outage probability of its transmission under the presence of a malicious jammer, while the jammer attempts to maximize the corresponding outage probability under its average power constraint. Optimal power control strategies for both source and jammer are obtained for minimax and maxmin problems, respectively, for any arbitrary finite number of blocks in block fading channels. Our results demonstrate the non-existence of Nash-equilibria of this two- person zero-sum game.

Wireless links form a critical component of communication systems that aim to provide ubiquitous access to information. However, the time-varying characteristics (or “state”) of wireless channels caused by the mobility of transmitters, receivers, and objects in the environment make it difficult to achieve reliable communication. Adaptive signaling exploits any channel state information (CSI) available at the transmitter to provide the potential to significantly increase the throughput of wireless links and/or greatly reduce the receiver complexity. As such, adaptive signaling has attracted significant research interest in the last decade and has found application in numerous commercial wireless systems, ranging from cellular data systems to wireless local area networks (WLANs). However, one of the great challenges of wireless communications is that it is difficult to obtain perfect CSI due to the inherently noisy and outdated nature of CSI available at the transmitter. Over the last decade, we have championed the idea of choosing the appropriate transmitted signal based on statistical models for the current channel state conditioned on the channel measurements. In this semi-tutorial paper, we first review how this class of methods has been developed for single-antenna systems, and then present novel recent designs for multiple-antenna systems. Key to the development in each case is the development of the error characterization given the outdated estimates and the use of such to allocate data rate and power over time and possibly space. In general, the focus is on rate allocation, while power allocation is done through a pruning method. Numerical results will demonstrate in both the single-antenna and multiple-antenna cases that such an approach provides a robust method for improving system data rate versus the standard practice of employing link margin to compensate for such uncertainties.

Synchronization of relay nodes is an important and critical issue in exploiting cooperative diversity in wireless networks. In this paper, two asynchronous cooperative diversity schemes are proposed, namely, distributed delay diversity and asynchronous space–time coded cooperative diversity schemes. In terms of the overall diversity–multiplexing (DM) tradeoff function, we show that the proposed independent coding based distributed delay diversity and asynchronous space–time coded cooperative diversity schemes achieve the same performance as the synchronous space–time coded approach which requires an accurate symbol-level timing synchronization to ensure signals arriving at the destination from different relay nodes are perfectly synchronized. This demonstrates diversity order is maintained even at the presence of asynchronism between relay node. Moreover, when all relay nodes succeed in decoding the source information, the asynchronous space–time coded approach is capable of achieving better DM tradeoff than synchronous schemes and performs equivalently to transmitting information through a parallel fading channel as far as the DM tradeoff is concerned. Our results suggest the benefits of fully exploiting the space–time degrees of freedom in multiple antenna systems by employing asynchronous space–time codes even in a frequency-flat-fading channel. In addition, it is shown asynchronous space–time coded systems are able to achieve higher mutual information than synchronous space–time coded systems for any finite signal-to-noise ratio (SNR) when properly selected baseband waveforms are employed.

In this paper, the peak-to-average power ratio (PAPR) is evaluated for the uncoded orthogonal frequency division multiplexing (OFDM) signals generated by using the IDFT procedure. The power spectral density of the transmitted OFDM signal is derived, taking into account the effects of cyclic prefix, windowing, digital-to-analog converter, null-subcarriers, and analog transmit filter. The PAPR distribution is then analyzed based on the extreme value theory. It is shown that the PAPR distribution is parameterized only by the length of the observation interval and the root mean square bandwidth of the signal, whose computation requires the PSD of the transmitted signal. Numerical results confirm that the proposed analysis accurately predicts the PAPR distribution of practical OFDM signals.

Relaying is often advocated for improving system performance by enhancing spatial diversity in wireless networks. Relay nodes make contributions to improving the source-destination link quality by sacrificing their own energy. In this paper, we address the issue of energy tradeoff made by relay nodes between transmitting their own data and forwarding other nodes' information in fading channels. Assuming channel state information (CSI) on fading amplitudes is perfectly known to both transmitters and receivers, we propose two power control and relaying policies. One is based on a strategic motivation, where each node functions as a relay and minimizes its own energy expenditure while meeting the outage probability requirement of all nodes. The second approach is based on complete collaboration, where the total energy consumption across all nodes is minimized. Numerical results demonstrate a significant impact of CSI on energy saving in relaying as compared with the relaying scheme without power control. In most cases, collaborative relaying dominates over the non-cooperative strategic one in the sense that the former not only minimizes total energy but also reduces individual energy expenditure of all nodes. This implies once forwarding and relaying is adopted across various nodes, exchanging of CSI becomes crucial, and collaborative energy minimization rather than the non-cooperative strategic approach should be pursued

Cooperative diversity, which employs multiple nodes for the simultaneous relaying of a given packet in wireless ad hoc networks, has been shown to be an effective means of improving diversity, and, hence, mitigating the detrimental effects of multipath fading. However, in previously proposed cooperative diversity schemes, it has been assumed that coordination among the relays allows for accurate symbol-level timing synchronization at the destination and orthogonal channel allocation, which can be quite costly in terms of signaling overhead in mobile ad hoc networks, which are often defined by their lack of a fixed infrastructure and the difficulty of centralized control. In this paper, cooperative diversity schemes are considered that do not require symbol-level timing synchronization or orthogonal channelization between the relays employed. In the process, a novel minimum mean-squared error (MMSE) receiver is designed for combining disparate inputs in the multiple-relay channel. Outage probability calculations and simulation results demonstrate the not unexpected significant performance gains of the proposed schemes over single-hop transmission, and, more importantly, demonstrate performance comparable to schemes requiring accurate symbol-level synchronization and orthogonal channelization.

Previous authors have shown that the asymptotic capacity of a multiple-element-antenna (MEA) system with N transmit and N receive antennas [termed an (N,N) MEA] grows linearly with N if, for all l, the correlation of the fading for two antenna elements whose indices differ by l remains fixed as antennas are added to the array. However, in practice, the total size of the array is often fixed, and thus the correlation of the fading for two elements separated in index by some value l will change as the number of antenna elements is increased. In this paper, under the condition that the size of an array of antennas is fixed, and assuming that the transmitter does not have access to the channel state information (CSI) while the receiver has perfect CSI, the asymptotic properties of the instantaneous mutual information I/sub N,N/ of an (N,N) MEA wireless system employing uniform linear arrays in a quasi-static fading channel are derived analytically and tested for accuracy for finite N through simulations. For many channel correlation structures, it is demonstrated that the asymptotic performance converges almost surely, implying that such MEA systems have a certain strong robustness to the instantiation of the channel fading values.

There has been significant interest in the capacity of multiple element antenna (MEA) wireless systems. Previous authors have shown that the asymptotic capacity of a system with N transmit and N receive antennas (termed an (N,N) MEA) grows linearly with N if, for all l, the correlation of the fading for two antennas whose indices differ by l remains fixed as antennas are added to the array. However, in practice, the total size of the array is often fixed, and thus the correlation of the fading for two elements separated in index by some value l changes as the number of antenna elements is increased. In this paper, under the condition that the length of a linear array of antennas is fixed, the asymptotic properties of the instantaneous mutual information I/sub N,N/ of an (N,N) MEA wireless system are derived analytically and tested for accuracy for finite N through simulations. Two different cases are considered: (1) when the fixed array size constraint is imposed at the mobile unit, and (2) when the fixed array size constraint is imposed at both the base station and the mobile unit. For the first case, simulation results indicate that the analytical approximations are very accurate for moderate values of N, especially at high signal-to-noise-ratios (SNR). For the second case, the predicted non-convergence of I/sub N,N/ is observed in simulations, as well.

Orthogonal frequency division multiplexing (OFDM) is a promising framework for future wireless communication systems. One of the main impediments that has limited the applicability of OFDM systems in low-power wireless communication systems is the highly variable amplitude of the baseband transmitted signal; thus, a number of previous analyses have characterized this variation. These analyses have generally employed the following two components: (1) the assumption that the complex envelope of the OFDM signal converges to a Gaussian random process in some sense as the number of subcarriers becomes large, and (2) Rice's (1945) classical results on level-crossing rates for the envelope of Gaussian random processes. In this work, we improve on both of these components to arrive at a simple, accurate, and rigorously-established expression for the peak distribution of the OFDM envelope. In particular, using a rigorous (and non-trivial) proof establishing the convergence in (1) above as justification, the modern extreme value theory for chi-squared processes is applied to the problem. Numerical results for both uncoded and coded systems establish that the simple expression obtained for the distribution of the peaks of the envelope process is extremely accurate, even for a modest number of subcarriers.

The measurement of the average received power is essential for power control and dynamic channel allocation in wireless communication systems. However, due to the effects of multipath fading and additive noise inherent to the wireless channel, there can be significant errors in such measurements. In this paper, the error statistics for average power measurements are considered; in particular, the probability distribution of the value of the average received power at the time of interest conditioned on an outdated measurement is obtained. The resulting expression should have high utility in the analysis of wireless communication systems. However, in this paper, the design of power control algorithms that minimize the average transmitted power required to achieve a desired outage probability for the link is considered. A number of novel power control algorithms based on various models for the error in the average power measurement are derived. Numerical results indicate that power control algorithms based on the accurate expression derived in this paper can demonstrate significant gains over those based on previous approximate models.

The measurement of the average received power is essential for power control and dynamic channel allocation in wireless communication systems. In this paper, the error statistics for average power measurements are considered; in particular, the probability distribution of the value of the average received power conditioned on a noisy measurement is obtained. The resulting expression should have high utility in the analysis of wireless communication systems; however, in this paper, this expression is employed in the design of power control algorithms that minimize the average transmitted power required to achieve a desired outage probability for the link. It is demonstrated that power control algorithms based on the accurate expression derived in this paper demonstrate significant gains over those based on previous approximate models.